Lime amendment to chronically acidified forest soils results in shifts in prokaryotic and fungal communities.
Bibliographic record
Abstract
Abstract A consequence of past acid rain events has been chronic acidification of both Nova Scotian forests and watersheds, leading to a loss of essential nutrients and subsequently to decreased forest productivity and biodiversity. Liming – supplementing forests with crushed rock (dolomite, limestone, or basalt) – can restore essential nutrients to acidified soils as well as increasing the pH of the soils and the carbon capture by forests by promotion of tree growth. The effectiveness of liming treatments have often been assessed biologically through tree growth measurements, but microorganisms respond rapidly to changes in pH and nutrient availability, and would potentially provide early insights into forest recovery. However, the impact of liming on the soil microbiome is not well understood; understanding the impacts of liming on a micro as well as a macro level will help to determine whether liming is a good remediation strategy for Nova Scotia. A pilot study evaluating liming in acidified forests in Nova Scotia began in 2017. Microbiome analyses (prokaryotic 16S rRNA and fungal ITS2 gene amplicon sequencing) of three different depths (horizons) of soil show significant differences between lime-treated and control soils for the prokaryotic but not fungal communities, particularly in the uppermost soil horizon sampled. Notably, several genera, particularly from the Bacteroidia class, were significantly more abundant in treated than control soils in both upper soil horizons. The impacts of liming treatment were smaller in the deepest soil horizon sampled, suggesting that lime amendment either takes longer to reach these depths, or has little impact on these microbial communities. Future studies that investigate the functional capacity of these microbial communities and longitudinal follow-ups are warranted.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".